Ant colony optimization for solving combinatorial fuzzy Job Shop Scheduling Problems

In this paper, we present an ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimized using ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the ant colony algorithm are evaluated. Computational results of the optimization algorithm are evaluated by analyzing the two popular JSSP benchmark instances, FT10 and the ABZ10 problems and the simulation is carried out using the software, MATLAB.